Agriculture, Ecosystems and Environment 118 (2007) 327–338
www.elsevier.com/locate/agee
Environmental impacts of farm scenarios according to five
assessment methods
Hayo M.G. van der Werf a,*, John Tzilivakis b, Kathy Lewis b,
Claudine Basset-Mens a
a
INRA, UMR Sol Agronomie Spatialisation de Rennes-Quimper, 65,
rue de Saint Brieuc CS 84215, 35 042 Rennes Cedex, France
b
Agriculture and Environment Research Unit (AERU), Science and Technology Research Institute,
University of Hertfordshire, College Lane Campus, Hatfield, Hertfordshire AL10 9AB, United Kingdom
Received 6 April 2005; received in revised form 31 May 2006; accepted 6 June 2006
Available online 4 August 2006
Abstract
It is not known to what extent the outcome of studies assessing the environmental impacts of agricultural systems depends on the
characteristics of the evaluation method used. The study reported here investigated five well-documented evaluation methods (DIALECTE,
Ecological Footprint, Environmental Management for Agriculture, FarmSmart, Life Cycle Assessment) by applying them to a case study of
three pig farm scenarios. These methods differ with respect to their global objective (evaluation of impact versus evaluation of adherence to
good practice), the number and type of environmental issues they consider, the way they define the system to be analysed, the mode of
expression of results (for the farm as a whole, per unit area or per unit product) and the type of indicators used (pressure, state or impact
indicators). The pig farm scenarios compared were conventional good agricultural practice (GAP), a quality label scenario called red label
(RL) and organic agriculture (OA). We used the methods to rank the three scenarios according to their environmental impacts. The relative
ranking of the three scenarios varied considerably depending on characteristics of the evaluation method used and on the mode of expression
of results. We recommend the use of evaluation methods that express results both per unit area and per unit product. Environmental evaluation
methods should be used with great caution, users should carefully consider which method is most appropriate given their particular needs,
taking into consideration the method’s characteristics.
# 2006 Elsevier B.V. All rights reserved.
Keywords: DIALECTE; Ecological footprint; Environmental management for agriculture; FarmSmart; Life cycle assessment; Organic agriculture; Label
rouge; Pig production
1. Introduction
Avariety of methods have been proposed for the evaluation
of the environmental impacts of farms (Von Wirén-Lehr,
2001; Van der Werf and Petit, 2002; Halberg et al., 2005). The
development of such methods is essential, as they can serve as
decision support tools for guiding the evolution towards more
sustainable agricultural production systems (Hansen, 1996).
* Corresponding author. Tel.: +33 2 23 48 57 09; fax: +33 2 23 48 54 30.
E-mail address: [email protected]
(H.M.G. van der Werf).
0167-8809/$ – see front matter # 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.agee.2006.06.005
These tools are increasingly used by farmers (Goodlass et al.,
2003), researchers (De Koeijer et al., 2002) and political
decision makers (Schröder et al., 2004).
The authors of studies using such methods to assess the
environmental impacts of agricultural systems rarely
acknowledge that the results obtained depend not only on
the characteristics of the systems compared, but also on
those of the evaluation method used. A methodological
reflection on the structure of methods for the environmental
evaluation of farms seems appropriate. Such methods
generally present five major stages (adapted from Petit
and van der Werf, 2003):
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H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338
1. Definition of the global objective of the method, e.g. The
evaluation of environmental impact or the evaluation of
adherence to good agricultural practice. This stage
involves choices with respect to the intended user, the
spatial scale for which the method is designed, and the
consideration of economic and social dimensions, in
addition to environmental impacts.
2. Definition of environmental objectives. Global objectives
cannot be directly assessed or quantified, a set of more
specific environmental objectives is required, which is at
the heart of the evaluation method (Van der Werf and
Petit, 2002). We define the term ‘‘environmental
objective’’ as an environmental issue of concern and
its associated desired trend. Other terms used for
environmental issues (OECD, 1999; EEA, 2005) are
environmental themes (Pointereau et al., 1999) and
impact categories (Guinée et al., 2002). Some examples
of environmental objectives: reduction of energy use,
reduction of emissions of nitrate or maintenance of soil
quality.
3. Definition of the system to be analysed. Many methods
are restricted to the evaluation of the direct impacts of a
system, by considering only the impacts from operation
of the system. Other methods also consider indirect
impacts, resulting from the production of the inputs
(fertilisers, feeds) to the system.
4. Construction or identification of indicators for each
environmental objective. To quantify the degree to which
the environmental objectives are attained, a set of
indicators serving as evaluation criteria is required. The
quality of an indicator will largely depend on the validity
of its calculation algorithm.
5. Calculation of results. Indicator values are calculated for
each of the production systems or scenarios to be
compared. A partial or total aggregation of results may
facilitate their interpretation.
These stages involves choices, in particular with respect
to the global objective of the method (stage 1), its
environmental objectives (stage 2), the way in which the
system is defined (stage 3), and concerning the indicators,
since for each environmental objective one or several
indicators are selected from many possible candidates
(stage 4).
Although the outcome of the assessment will obviously
be affected by these choices, they are rarely discussed
or justified by those proposing such methods. Methods
show great variability with respect to the implementation of these choices. For instance, a review of 12
methods used for the evaluation of environmental impacts
at the farm level revealed that the number of environmental objectives considered per method varied from 2 to
13 (Van der Werf and Petit, 2002). Of the total of 26
objectives, some were considered in six or seven methods,
whereas others were considered in a single method
only.
Although different methods for environmental evaluation
have been compared on the basis of their published
descriptions (Von Wirén-Lehr, 2001; Van der Werf and
Petit, 2002; Braband et al., 2003; Halberg et al., 2005), we
did not find any comparative studies based on the actual
application of different methods to a set of farms or farm
scenarios.
The study reported here investigated five well-documented evaluation methods by applying them to a case study of
three farm scenarios. The objectives of the study were to
examine to what extent the five methods produce different
results and to investigate which characteristics of the
methods affected the outcome. It should also allow us to
propose recommendations for the selection of evaluation
methods.
2. Materials and methods
2.1. Farm scenarios
This study compared three contrasting scenarios of farms
producing crops and pigs in Bretagne, western France,
showing major differences with respect to crop production
practices and input use, animal housing systems and crop
and animal production levels (Table 1). Technical performance, input use and emissions to the environment used to
construct the scenarios were based mainly on published data,
complemented by real farm data supplied by a range of
experts (Basset-Mens and van der Werf, 2005).
The good agricultural practice (GAP) scenario corresponds to a current intensive (or ‘‘conventional’’) pig
production farm, optimised in particular with respect to
fertilisation practices, as specified in the French ‘‘Agriculture Raisonnée’’ standards (Rosenberg and Gallot, 2002). In
the GAP scenario, pigs are raised in a slatted-floor building.
The organic agriculture (OA) farm scenario corresponds to
organic agriculture according to the French version of the
European rules for organic animal production (Ministère de
l’Agriculture et de la Pêche, 2000) and the European rules
for organic crop production (CEE, 1991). The Red Label
(RL) farm scenario corresponds to the ‘‘Porc Fermier Label
Rouge’’ quality label (Groupement des Fermiers d’Argoat,
2000). In the OA and RL scenarios, pigs are born and raised
outdoors until weaning, and in an open-front straw-litter
building at low animal density after weaning. An inventory
of some of the main emissions for the three farm types is
presented in Table 2.
Details on crop and feed production practices and on
animal production practices can be found in Basset-Mens
and van der Werf (2005). For the three scenarios we assumed
that farmers adopt good agricultural practice, and respect
current regulations. Some of the evaluation methods applied
here, in particular EMA, but also FarmSmart, require
extensive information with respect to the farm conservation
practices (e.g. concerning hedgerows, field margins, and
H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338
329
Table 1
Characteristics of the good agricultural practice (GAP), red label (RL) and organic agriculture (OA) farm scenarios
Crop production
Farm size (ha)
Annual crops
Production of cereal straw
GAP
RL
OA
68
Pea, winter triticale,
winter wheat
261 t, sold off farm
38.3
Grain maize, winter barley,
winter triticale
67 t, used as animal bedding;
an additional 62 t bought off-farm
Ryegrass–clover paddock (10 ha)
40
40
52.8
Horse bean, grain maize, spring barley,
winter oats, winter wheat
96, 65 t of which used for animal
bedding, no straw sold
Ryegrass–clover paddock
(4.4 ha), lucerne (4.8 ha)
70
Slatted-floor
150
21.1
25.7
1313
Outdoor
67
18.9
28
1490
Outdoor
40
17.7
42
1695
Slatted floor
0.85
275
113
4857
Straw litter
2.6
312
115
3530
Straw litter
2.3
340
120
1480
Perennial crops
% weight of pig feed produced on-farm
Animal production: piglet production
Housing
Herd size (no. of sows)
Weaned piglet/sow/year
Weaning age (days)
Feed/sow (boars incl.) (kg/year)
Animal production: weaning to slaughtering
Housing
Surface per pig (m2)
Feed consumed (kg)
Slaughter weight (kg)
Pig live weight produced/ha of farm
surface/year
watercourses), in this respect the farm scenarios were
assumed identical.
For the three scenarios, all crops produced were used as
components of the concentrate feed for the pigs. For GAP
and RL, the crops produced on-farm contributed 40% to the
weight of the concentrate feed used, for OA 70%. The three
scenarios strongly differ in pig live weight production per ha
of farm surface per year (Table 1): 4857 kg for GAP, 3530 kg
for RL, and 1480 kg for OA. Input use (additional pig feed,
fertiliser, electricity, diesel) per ha was largest for GAP and
smallest for OA.
2.2. Evaluation methods
In the recent past, farms had a single principal function:
production of food and fibre for the market. Nowadays farms
have a second main function, which becomes increasingly
important: production of non-market goods (e.g. environmental services). In the evaluation of the environmental
impacts of farms, both functions should be considered. In
this study we expressed farm impacts, when methods
allowed, by two functional units: on the one hand per unit
area, reflecting the farm’s function as a producer of nonmarket goods, and on the other hand per unit product,
reflecting its function as a producer of market goods.
2.2.1. Life cycle assessment (LCA)
LCA is a technique to evaluate the environmental burdens
associated with a product, process, or activity. In the
inventory analysis phase the resources consumed and the
emissions to the environment, both on-farm and associated
with the production and delivery of the inputs used on the
farm, are listed. In the impact assessment phase, resources
used and emissions are interpreted in terms of environmental
Table 2
Inventory of some of the main emissions according to Basset-Mens and van der Werf (2005), expressed per hectare of land used (both on and off farm) and per kg
of pig, for the three farm scenarios
Emission
Unit (kg)
Per hectare of land used
GAP
Nitrate
Oxides of nitrogen
Ammonia
Sulphur dioxide
Methane
Nitrous oxide
Carbon dioxide
Carbon monoxide
a
NO3
NOx
NH3
SO2
CH4
N2O
CO2
CO
203
15.9
43.5
5.7
40.3
5.7
1625
5.5
RL
181
14.5
16.4
7.5
14.2
11.0
1783
4.9
For each substance, lowest value per ha of land used and per kg of pig in bold.
Per 1000 kg of pig
OA
a
127
14.6
17.7
4.8
12.4
7.6
1408
4.9
GAP
RL
OA
110
8.6
23.6
3.1
21.9
3.1
882
3.0
114
9.1
10.3
4.7
8.9
6.9
1120
3.1
125
14.4
17.4
4.7
12.2
7.5
1390
4.8
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H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338
impacts (Guinée et al., 2002), by multiplying the aggregated
resources used and the aggregated emissions of each
individual substance with a characterisation factor for each
impact category to which it may potentially contribute.
The results presented here are based on a detailed LCA
study of pig production (Basset-Mens and van der Werf,
2005), which expressed results per kg of pig live weight
produced and per ha of land used (i.e. including land offfarm, used for the production of crop-based ingredients for
concentrate feed).
2.2.2. Ecological footprint (EF)
‘‘The ecological footprint of a designated population is
the area of productive land and water ecosystems required to
produce the resources that the population consumes and to
assimilate the wastes that the population produces, wherever
on Earth the land and water is located’’ (Rees, 2000). The
area of productive land and water ecosystems available per
capita is designed as a fair Earth share, and constitutes a
threshold value which can be used as a benchmark in an EF
analysis (Wackernagel and Rees, 1996).
We calculated the farm EF as the sum of four
components. The first component is the land surface of
the farm being assessed; the second is the land surface
required to produce the ingredients of the concentrate feed
which were not produced on-farm. The latter surface was
calculated using average yields for 1996–2000 (FAO, 2002).
The third component, ‘‘energy land’’, corresponds to the
land required to produce the non-renewable energy used on
the farm and for the production and delivery of the farm
inputs. This was calculated assuming a net productivity of
80 GJ/ha (Wackernagel and Rees, 1996). The fourth
component, ‘‘carbon-sink land’’, reflects the land area
needed to sequester the CO2 corresponding to the greenhouse gasses emitted on the farm and for the production and
delivery of the farm inputs. Greenhouse gasses resulting
from the use of non-renewable energy were not included
here, as these were taken care of in the ‘‘energy land’’
component. CO2-equivalents were calculated according to
the GWP100 factors by IPCC (Houghton et al., 1996) in kg
CO2 equiv.: N2O: 310, CH4: 21. An annual CO2 sequestration rate of 6.6 t ha1 was assumed (Wackernagel and Rees,
1996).
2.2.3. Environmental management for agriculture
(EMA)
Lewis and Bardon (1998) proposed EMA, ‘‘a computerbased informal environmental management system for
agriculture’’. The core of the system is the performance
assessment (PA) mode, which compares actual farmer
production practices and site-specific details with regulatory
compliance and what is perceived to be best practice for that
site. EMA has been designed on a modular basis, with each
module producing a report and an environmental performance index, known as an eco-rating, for a specific aspect of
farming. Within each module, where appropriate, an
estimate of emissions is made, which, when collated, forms
an emissions inventory (EI) for the farm being assessed
(Lewis et al., 1999). EMA seeks to encourage continuous
improvement in environmental performance, tackling issues
and problems in small steps, that are practically manageable
and financially affordable.
Its technical system is a support mode, which incorporates modules to explore ‘‘What-If’’ scenarios, to identify
site-specific solutions to environmental problems and so
improve future eco-ratings. This mode helps the user
identify solutions to problems spotted in the performance
assessment mode. The second support mode is a hypertext
advisory system. In this study the EMA 2004 version was
used.
2.2.4. FarmSmart
In 2000 the UK government launched a pilot set of
agricultural sustainability indicators to provide a means of
measuring the economic, social and environmental impacts
of agriculture in Great Britain at national level (Tzilivakis
and Lewis, 2004). It was hoped that stakeholders would find
the indicators valuable for regional and local use. However,
many of the indicators have been defined from the policy
top-down perspective, some are not measurable directly on
farm, and few have direct links with on-farm management
decisions. Consequently, the key messages emerging from
the indicators can easily be lost at farm level.
In order to address these issues, FarmSmart, a simple tool
for farmers, was developed (Tzilivakis and Lewis, 2004). It
collates relevant information to identify appropriate
indicator values for a specific farm and location and provide
a management focus, such that farmers are provided with
information to help them select indicators relevant to their
situation, assess their performance, and take steps for
improvements where required.
The method yields 35 indicators referring to Economy,
Management, Inputs, Resources and Conservation. In this
study results are presented for those indicators showing
differences for the farms compared. The FarmSmart Beta
version 1.0.2 was used.
2.2.5. DIALECTE
Solagro (2000) proposed DIALECTE for the evaluation
of the environment at farm level by means of a
comprehensive, simple and rapid approach. This method
is an improved version of the ‘‘Solagro Diagnostic’’
proposed by Pointereau et al. (1999). The method yields
16 agro-environmental indicators (AEI) allowing a rapid
and global evaluation of the environmental risks of the farm.
It further produces a whole farm approach (WFA) consisting
of an Energy analysis, of performance levels for Farm
diversity and Management of inputs, and of an assessment of
the potential impacts of the farm on water, soil, biodiversity
and resource use. The method can be applied to all
agricultural production systems in France. In this study
DIALECTE version 4.0 (January, 2004) was used. No
H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338
331
3.1.2. Environmental objectives
Environmental objectives were grouped in four classes
(Van der Werf and Petit, 2002): farming practice-related,
input-related, emission-related and system state-related. The
methods compared differ with respect to the number and type
of environmental objectives taken into account (Table 3).
LCA, EF and FarmSmart consider input-related and
emission-related objectives. EMA considers farming practice-related and emission-related objectives, whereas DIALECTE considers farming practice-related, input-related
and system state-related objectives. EF is narrow in focus, as
only three objectives are considered. EMA (12 objectives)
and DIALECTE (19 objectives) are wide ranging, and are
the only methods taking into account objectives related to
farming practices. FarmSmart and LCA (both six objectives)
results were presented for three of the 16 Agro-Environmental indicators (livestock units/ha of forage crop, irrigated
surface, and length of the grazing season), as they were not
relevant for the farms compared here.
3. Results
3.1. Characterisation of the methods
3.1.1. Global objective of the method
LCA, EF, FarmSmart and DIALECTE share the same
global objective: the evaluation of environmental impact,
whereas EMA’s global objective is the assessment of
adherence to best practice.
Table 3
Characterisation of the evaluation methods with respect to their environmental objectives, which were grouped as: farming practice-related, input-related,
emission-related, related to the state of the system
Environmental objectives
Methodsa
LCA
EF
EMA
PA
Farming practice related
Fertiliser usage \ b
Organic manure management \
Odour management \
Pesticide usage and general management \
Pesticide treatment frequency #
Overall soil management \
Growing of legume crops and grass \
Crop diversity \
Soil cover by crops in winter "
On-farm production of feed "
Livestock husbandry \
Livestock diversity \
Energy and water efficiency \
Farmland conservation \
Input related
Use of non-renewable energy #
Land use #
Water use #
N fertiliser use #
P fertiliser use #
N balance (input–output) #
P balance (input–output) #
Pesticide use #
Emission related
Emission of greenhouse gases #
Emission of acidifying gases #
Emission of eutrophying substances #
Emissions concerning terrestrial ecotoxicity #
System state related
Landscape quality
Agricultural biodiversity
Water quality
Soil quality
FarmSmart
EI
x
x
x
x
DIALECTE
AEI
WFA
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
An x indicates that the objective is taken into account.
a
LCA, life cycle assessment; EF, ecological footprint, EMA, environmental management for agriculture; PA, performance assessment; EI, emissions
inventory; AEI, agro-environmental indicators; WFA, whole farm approach.
b
#, objective to be minimised; \, objective to be optimised; ", objective to be maximised.
332
H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338
Table 4
Indicator types for the evaluation methods
Indicator type
Methods
LCA
Pressure
State
Impact
x
EF
EMA
FarmSmart
PA
EI
x
x
x
DIALECTE
AEI
WFA
x
x
x
x
x
to Impacts on human health and ecosystems, which may elicit
a societal Response.
None of the methods use Driving forces or Responses
indicators (Table 4). LCA and EF use Impact indicators,
EMA and FarmSmart use Pressure indicators, whereas
DIALECTE uses both Pressure and State indicators. Thus,
the methods compared here differ with respect to the types of
indicators used.
For method acronyms see Table 3.
3.2. Evaluation of the farm scenarios
are of intermediate scope, with FarmSmart relying mainly
on input-related objectives, and LCA more on emissionrelated objectives. The methods compared thus are quite
distinct with respect to the number and the type of
environmental objectives considered.
3.1.3. Definition of the system
For EMA and DIALECTE, the system evaluated consists
of the farm only, whereas for LCA, EF and FarmSmart the
system includes the production of inputs.
3.1.4. Indicators used
Indicators serve as criteria to quantify the degree to which
environmental objectives are attained. The indicators used by
the five methods were categorised according to the Driving
forces–Pressures–State–Impact–Responses (DPSIR) framework proposed by the European Environmental Agency
(EEA, 1999). According to this view, social and economic
Driving forces exert Pressure on the environment, as a
consequence the State of the environment changes, this leads
The results for each method will be presented in this
section, and the three scenarios will be ranked. We use
ranking here as a tool to ‘‘condense’’ the extensive and
diverse output produced by the different methods in order to
be able to compare their results for the three scenarios. This
does not imply that we consider ranking of farming systems
to be the primary objective of these methods.
A ranking of the three scenarios is only uncontroversial in
the case where all indicators within a given evaluation
method separately rank the three scenarios in the same order.
This was, however, never the case. Our ranking was mainly
based on the number of ‘‘best’’ and ‘‘worst’’ scores the
scenarios obtained, details are given below. We implicitly
considered thus that all indicators are equally important,
which is a subjective choice and will not necessarily be true
in ‘‘the real world’’. When ‘‘best’’ and ‘‘worst’’ scores were
more or less evenly distributed among the scenarios, we
attributed identical ranks. This procedure obviously is not
100% objective, but it is transparent (since the full
information is available in Tables 5–11) and allows an
Table 5
The environmental impacts calculated according to Basset-Mens and van der Werf (2005) using life cycle assessment (LCA), expressed per ha of land used and
per kg of pig produced for the three farm scenarios
Impact category
Eutrophication
Climate change
Acidification
Terrestrial toxicity
Non-renewable energy use
Land use
a
Unit
Per hectare of land used
kg PO4-equiv.
kg CO2-equiv.
kg SO2-equiv.
kg 1.4-DCB-equiv.
MJ LHV
m2 year
Per kg of pig
GAP
RL
OA
GAP
RL
OA
38.3
4236
80.1
30.4
29282
10000
26.4
5510
36.0
29.3
28503
10000
21.9a
4022
37.7
30.8
22492
10000
0.0208
2.30
0.0435
0.0165
15.9
5.43
0.0166
3.46
0.0226
0.0184
17.9
6.28
0.0216
3.97
0.0372
0.0304
22.2
9.87
For each impact category lowest value per ha of land used and per kg of pig in bold.
Table 6
The ecological footprint of the three farm scenarios, values in ha year per ha of farm surface and in ha year per 1000 kg of pig live weight
Footprint component
Per hectare of farm surface
Per 1000 kg of pig
GAP
RL
OA
GAP
RL
OA
Farm
Additional pig feed
Non-renewable energy
Carbon sink
1
1.63
0.96
1.05
1
0.96
0.79
1.25
1
0.32
0.41
0.58
0.21a
0.33
0.20
0.22
0.30
0.29
0.23
0.37
0.67
0.22
0.28
0.39
Total footprint
4.64
4.00
2.31
0.96
1.19
1.56
a
For each component lowest value per ha and per kg of pig in bold.
H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338
Table 7
The eco-ratings calculated using the environmental management for agriculture (EMA) performance assessment mode for the three farm scenarios
Eco-rating
Range
GAP
RL
OA
Fertiliser usage
Organic manure
Odour management
Crop pesticide usage
General pesticide management
Overall soil management
Indoor pigs
Outdoor pigs
Energy efficiency
Water efficiency
Farmland conservation
Average eco-rating
+100/100
+100/100
0/100
0/100
0/100
+100/100
+100/100
+100/100
+100/100
+100/100
+100/100
19
S2
S20
11
S19
30
39
n.a.
S44
40
24
4.2
28
3
31
S9
20
30
70
5
44
40
24
3.3
8a
3
31
n.a.
n.a.
30
70
5
44
40
24
3.2
333
results were expressed per kg of pig produced, the picture
was very different: OA did worst for all impacts with the
exception of acidification, for which GAP had the highest
impact. Overall GAP did best, as it had the lowest values for
four impacts; RL had the lowest values for two impacts, here
we rank scenarios GAP > RL > OA.
overall assessment of the three scenarios by the five methods
(Section 3.3, Table 12).
3.2.2. Ecological footprint
Expressed per ha of farm surface, OA had the smallest
Ecological Footprint, and GAP the largest (Table 6).
Footprint components (excluding farm surface, which –
by definition – was identical across scenarios) were smallest
for OA, RL had the largest value for carbon sink land and
GAP for the other two components. Overall we rank
OA > RL > GAP. Expressed per kg of pig, results were
inverted, now GAP had the smallest footprint and OA the
largest. GAP had the lowest values for all footprint
components except land for additional pig feed, where
OA had the smallest value. We rank GAP > RL > OA.
3.2.1. Life cycle assessment
When LCA results were expressed per ha of land used,
the OA scenario did best: it had lowest impact values for
eutrophication, climate change and energy use. GAP had
highest values for all impacts except climate change
(Table 5), so we rank scenarios OA > RL > GAP. When
3.2.3. EMA
The eco-ratings did not clearly differentiate the scenarios
(Table 7). GAP did best for three eco-ratings, both RL and
OA did best for two eco-ratings, differences were often
minor. The average eco-rating was slightly better for OA
than for the other two scenarios. RL and OA had worse eco-
a
For each eco-rating highest value in bold.
Table 8
The emissions inventory calculated using environmental management for agriculture (EMA), expressed per hectare of farm surface and per 1000 kg of pig
produced, for the three farm scenarios
Emission
Unit (kg)
Per hectare of farm surface
GAP
Minimum potential nitrate leaching
Oxides of nitrogen
Ammonia
Sulphur dioxide
Methane
Carbon dioxide
Carbon monoxide
a
NO3
NOx
NH3
SO2
CH4
CO2
CO
104
9
61
13
22
1565
1
RL
150
14
41
0
18
421
0
Per 1000 kg of pig
OA
0
6
14
0
8
337
0
a
GAP
RL
OA
21.4
1.8
12.5
2.7
4.5
322
0.21
42.5
3.9
11.7
0
5.2
119
0
0
3.8
9.4
0
5.1
228
0
For each emission lowest value per ha and per 1000 kg of pig in bold.
Table 9
Values of farm-level indicators, expressed per hectare of farm surface and per 1000 kg of pig produced, calculated using FarmSmart for the three farm scenarios
Indicator
Unit
Per hectare of farm surface
GAP
Pesticide active ingredient used
Growth regulator active ingredient used
N fertiliser use
P fertiliser use
Ammonia emission
Methane emission
Nitrous oxide emission
Carbon dioxide emission
Direct energy consumption
Indirect energy consumption
a
kg
kg
N (kg)
P2O5 (kg)
NH3 (kg)
CH4 (kg)
N2O (kg)
CO2 (kg)
GJ
GJ
2.19
0.79
20.3
14.9
85.3
92.8
16.1
2485
21.5
21.1
For each indicator lowest value per ha and per 1000 kg of pig in bold.
RL
1.88
0.31
53.5
0
66.8
71.9
16.4
1830
6.7
19.7
Per 1000 kg of pig
OA
a
0
0
0
0
25.6
28.1
4.2
562
4.8
3.4
GAP
RL
OA
0.45
0.16
4.2
3.1
17.6
19.1
3.3
512
4.4
4.3
0.56
0.09
16.0
0
19.9
21.5
4.9
518
1.9
5.6
0
0
0
0
17.3
19.0
2.8
380
3.2
2.3
334
H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338
Table 10
Values of agro-environmental indicators calculated using DIALECTE for the three farm scenarios
Indicator
Grass > 2 year
N from manure on surfaces receiving manure
N from manure/total N
Surface receiving manure
Length of hedges and woodland borders
Direct energy use in diesel litre equivalents
N balance (input–output)
P2O5 balance (input–output)
K2O balance (input–output)
Number of species grown
Pesticide treatment frequency indexc
Surface without crop cover on December 31
Legume crop surface
a
b
c
Unfavourable a
Unit
%
kg/ha
%
%
m/ha
l/ha
kg/ha
kg/ha
kg/ha
score
ha/ha
%
%
0
340
0
0
0
300
100
100
100
1
4
100
0
Favourable
GAP
100
0
100
100
100
0
0
0
0
9
0
0
40
RL
OA
b
0
231
89
68
70
499
69
44
78
3
9.2
16
32
26
194
74
74
70
178
103
39
64
5
5.5
17
13
17
73
100
80
70
120
1
3
S11
4
0
0
16
To guide interpretation, values considered unfavourable and favourable are indicated.
For each indicator most favourable value in bold.
Average number of standard pesticide treatments used by area and year. Standard treatment is the approved dosage for a certain crop.
ratings for odour management than GAP, which is
surprising, as it is generally perceived that pig production
on straw litter (as for OA and RL) causes less odour
problems than pig production on slatted floors (as for
GAP) (Paul Robin, pers. comm., 2004). We rank
GAP RL OA.
Expressed per ha of farm surface, the EMA emission
inventory consistently yielded the lowest values for OA,
whereas GAP had highest values for five of the seven
substances considered (Table 8), we rank OA > RL > GAP.
Per kg of pig produced, no scenario clearly stood out. OA
showed the lowest values for nitrate loss and ammonia loss,
for two other emissions it shared the lowest values with RL.
RL had the lowest carbon dioxide emissions, while GAP
emitted least oxides of nitrogen and methane. We rank
GAP RL OA.
3.2.4. FarmSmart
Expressed per ha of farm surface, OA showed the
lowest values for all indicators, whereas GAP had
the highest value for eight indicators out of ten
(Table 9), we rank OA > RL > GAP. Per kg of pig, OA
showed the lowest value for nine indicators out of ten, but
here RL did rather worse than GAP, as it had the highest
value for seven indicators, we therefore rank OA >
GAP > RL.
3.2.5. DIALECTE
For the agro-environmental indicators presented here,
OA showed the most favourable value in eight out of thirteen
cases, whereas both RL and GAP presented the most
favourable value for two indicators each (Table 10). We rank
OA > RL GAP.
Table 11
Values calculated using the DIALECTE whole farm approach for energy efficiency, farm diversity, input management and potential farm impacts for the three
farm scenarios
Indicator
Unit
Maximum score
Energy efficiency, output (in meat)/input
1.17
Farm diversity
Crop diversity and soil cover
Livestock diversity, autonomy and fertility transfer
Natural elements and space
Score
Score
Score
Input management
Nitrogen
Phosphorous
Water
Pesticides
Energy
Score
Score
Score
Score
Score
Potential farm impact on:
Water (quality and quantity)
Soil (fertility, erosion control)
Biodiversity (animal and plant)
Resource use
Score
Score
Score
Score
Total score
a
For each indicator highest score in bold.
GAP
30
22
18
7.5
3
6
7.5
6
19
5
1
RL
1.20
17
5
2
OA
1.59a
19
6
5
0.5
0
6
0.1
3
0.8
0.8
6
1.6
3
7.5
2.9
6
7.5
5.3
20
20
20
20
8
9.4
0.8
9
9
11.7
0.8
15
16
12.6
5.8
17
180
61.8
72.7
110.6
H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338
335
Table 12
Ranking of the three farm scenarios by the five evaluation methods
Mode of expression
Methodsa
Scenario
LCA
EF
EMA
FarmSmart
PA
Farm as a whole
GAP
RL
OA
Expressed per hab
GAP
RL
OA
GAP
RL
OA
Expressed per kg of pig live weight
EI
1
1
1
3
2
1
1
2
3
3
2
1
1
2
3
3
2
1
1
1
1
DIALECTE
AEI
WFA
2
2
1
3
2
1
3
2
1
2
3
1
a
For EMA the performance assessment (PA) and emissions inventory (EI) modes are distinguished; for DIALECTE the agro-ecological indicators (AEI) and
whole farm approach (WFA) are distinguished. For each mode of expression numbers within a column indicate ranks with 1, best; 3, worst.
b
Per ha of land used (including land off-farm) for LCA, per ha of farm surface for EF, EMA, and FarmSmart.
According to FarmSmart, OA did best, no matter whether
results were expressed per ha or per kg of pig produced.
When results were expressed per ha GAP did worse than RL,
when results were expressed per kg of pig, RL did worse
than GAP (Table 12).
At the whole farm level, DIALECTE ranked OA first,
both through its agro-environmental indicators and its whole
farm approach. The agro-environmental indicators ranked
GAP and RL similarly, whereas, in the whole farm approach,
RL had better scores than GAP.
Within the whole farm approach, OA presented the
highest and GAP the lowest energy output/input ratio
(Table 11). For Farm diversity, Input management and
potential farm impacts, OA showed the highest (i.e. best)
scores for all indicators (Table 11). For seven indicators
GAP had lowest scores and for one indicator RL had the
lowest score. We rank OA > RL > GAP.
3.3. Ranking of the farm scenarios
In order to obtain a view of the overall assessment of the
three scenarios by the five methods, the rankings proposed
in Section 3.2 (Tables 5–11) have been summarised in
Table 12. Depending on the method, results were expressed
for the farm as a whole, per ha, and per kg of pig live
weight.
LCA and EF produced identical rankings: when results
were expressed per ha, OA did best and GAP worst,
expressed per kg of product GAP did best and OA worst
(Table 12). At the farm level, EMA established similar
environmental performance for the three scenarios. In the
EMA emissions inventory however, OA did best and GAP
worst. When emissions were expressed per kg of pig
produced, no clear differentiation emerged (Table 12).
3.4. Emissions inventories
Three methods produced emissions inventories, which are
summarised in Table 13. For GAP and LR, LCA and EMA
produce values that are reasonably close, whereas FarmSmart
values (when available) are consistently higher. For OA, EMA
produces lower values than LCA and FarmSmart.
These differences in levels of emissions per unit surface
should be related to the way the three methods define the
system under evaluation and to the extent to which they
consider off-farm emissions. LCA expresses results per ha of
land used (including land off the farm) and considers off-farm
emissions associated with a wide range of inputs: the
Table 13
Emission inventories according to LCA (per ha land used), EMA-EI and FarmSmart (both per ha of farm surface), for the three farm scenarios
Emission
Unit (kg)
LCA
GAP
Nitrate
Oxides of nitrogen
Ammonia
Sulphur dioxide
Methane
Nitrous oxide
Carbon dioxide
Carbon monoxide
a
NO3
NOx
NH3
SO2
CH4
N2O
CO2
CO
203
15.9
43.5
5.7
40.3
5.7
1623
5.5
For each substance, lowest value in bold.
EMA
RL
181
14.5
16.4
7.5
14.2
11.0
1783
4.9
OA
a
127
14.6
17.7
4.8
12.4
7.6
1408
4.9
FarmSmart
GAP
RL
OA
GAP
RL
OA
104
9
61
13
22
–
1565
1
150
14
41
0
18
–
421
0
0
6
14
0
8
–
337
0
–
–
85.3
–
92.8
16.1
2485
–
–
–
66.8
–
71.9
16.4
1830
–
–
–
25.6
–
28.1
4.2
562
–
336
H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338
construction of pig housing, the production and delivery of
concentrate pig feed (including growing of crops), and of
fertilisers, pesticides, agricultural machines and energy
carriers, including all sea and road transport involved.
FarmSmart also considers a wide range of inputs, including
concentrate feed, but emitted substances considered are
limited to CO2 and N2O, while emissions are expressed per ha
of farm surface. This means, for instance, that nitrate leaching
associated with the crops for the concentrate pig feed was part
of the system in the LCA approach, but not in FarmSmart.
EMA finally does not include any off-farm emissions
associated with inputs and expresses emissions per ha of
farm surface. In addition to these differences in system
definition, the use of different algorithms or emission factors
has further contributed to differences among the inventories.
4. Discussion
4.1. Characterisation of scenarios and methods
The three farm scenarios evaluated here differ strongly,
both in input use (e.g. non renewable energy use was
33 GJ ha1 for OA and 77 GJ/ha1 for GAP (Table 5),
purchase of concentrate feed was 1.7 t ha1 for OA and
9.0 t ha for GAP, data not shown), and in output (pig
production per ha of farm surface was 1480 kg for OA and
4857 kg for GAP, Table 1). Thus, in relative terms, OA can
be characterised as ‘‘low input–low output’’, GAP as ‘‘high
input–high output’’, with RL being intermediate.
The methods consider different environmental objectives
and use different types of indicators. LCA and EF both
consider input-related and emission-related objectives
(Table 3), quantified by impact indicators (Table 4). EF
(three objectives), has a more narrow focus than LCA (six
objectives).
EMA-PA deals uniquely with farming practice-related
objectives, it is wide-ranging (eight objectives), whereas
EMA-EI deals with four emission-related objectives. Both
EMA-PA and EMA-EI use pressure indicators. FarmSmart,
as LCA and EF, relies on input-related and emission-related
objectives, which are quantified through pressure indicators.
DIALECTE-AEI (11 objectives) and DIALECTE-WFA (13
objectives) are the only methods based on system staterelated objectives, in addition to farming practice and input
use-related objectives. These methods use both pressure and
state indicators.
4.2. Ranking of the scenarios
We used the methods to rank the three scenarios
according to their environmental impacts. Depending on
the method used and on the way results were expressed (for
the farm as a whole, per ha or per kg product), ranking from
best to worst was OA > RL > GAP, or its inverse:
GAP > RL > OA, or ranking proved inconclusive.
For three methods (EMA-PA, DIALECTE-AEI, DIALECTE-WFA) rankings were established at the scale of the
farm as a whole (Table 12). EMA-PA did not differentiate
among the three scenarios, whereas DIALECTE-AEI and
DIALECTE-WFA both ranked OA best, with DIALECTEWFA ranking GAP worst, while DIALECTE-AEI attributed
similar ratings to GAP and RL. EMA’s global objective
(assessment of adherence to best practice) suffices to explain
its lack of differentiation of the three scenarios, since all
three confess adherence to good practice.
Four methods (LCA, EF, EMA-EI, FarmSmart) allowed
expression of results both per unit area and per unit product
(Table 12). These methods are based on a limited number
(three to six) of input-related and emission-related environmental objectives, several of which are shared (Table 3). The
methods use impact and pressure indicators (Table 4). With
results expressed per ha, all four methods ranked OA (low
input–low output) best and GAP (high input–high output)
worst (Table 12). However, with results expressed per kg of
pig, rankings were not identical. LCA and EF ranked GAP
best and OA worst, whereas FarmSmart ranked OA best and
RL worst, while EMA-EI did not differentiate the scenarios.
These results deserve a closer analysis.
Increased use of inputs (e.g. fertilisers, concentrate feed)
per unit area allows higher output of desired products, but
also inevitably leads to more undesired outputs, i.e.
emissions to the environment (e.g. De Koeijer et al.,
2002; Schröder et al., 2003; Lewis et al., 2003). As a result,
on a per area basis, impacts will logically tend to increase
with increasing level of input use of the farm. This was
confirmed here by the four methods producing identical
rankings (OA > RL > GAP) when results were expressed
per unit area. However, when impacts are expressed per kg
of product, the correlation between input use per ha and
impacts will depend on the ratio of undesired outputs
(emissions to the environment) over desired outputs
(products), both of which increase with input use, but not
necessarily at the same rate. In the case-study presented
here, the four methods did not produce identical rankings
when impacts were expressed per unit product, illustrating
greater sensitivity of this mode of expression to differences
among the methods for environmental objectives considered
and calculation algorithms used for the indicators.
The results of the methods compared here clearly vary.
Two main sources of difference can be distinguished
(Table 12): (a) differences between modes of expression
(e.g. LCA per ha versus LCA per kg) and (b) differences
between methods within a mode of expression (e.g. EMAPA versus DIALECTE-AEI).
4.3. Differences between modes of expression
The mode of expression of results had a major effect on
rankings of the scenarios. The question whether impacts of
agricultural production systems should be expressed per unit
area or per unit product has been subject of considerable
H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338
debate. From the LCA point of view (Guinée et al., 2002),
impacts should be expressed per unit product when the
function of the system is the production of commodities, and
per unit area for a non-market function (e.g. environmental
services). Haas et al. (2000) have argued that for local/
regional impacts, such as eutrophication, expression per unit
area is most appropriate, whereas for global impacts (e.g.
climate change) impacts should be expressed per unit
product. De Koeijer et al. (2002) prefer expression of
impacts per unit area to take into account the carrying
capacity of the environment. We feel that there is a strong
case for expressing impacts of agricultural production
systems both per unit area and per unit product.
The impact/unit area ratio combines an environmental
criterion and an area, the latter supplying a context, allowing
the implementation of area-based threshold values founded
on critical limits or ceilings, which can be derived from
national or regional goals for emissions or impacts (IPCC,
2001; Erisman et al., 2003). The impact/unit product ratio
combines an environmental criterion and a production
criterion, and thus is a measure of environmental efficiency
(Olsthoorn et al., 2001), rather than a measure of
environmental impact.
The two modes of expression are clearly complementary.
Reliance on the sole impact/unit area ratio may well lead to a
preference for low input–low output systems, which may
decrease impacts at regional level, but may create a need for
additional land use elsewhere, giving rise to additional
impacts. On the other hand, reliance on the impact/unit
product ratio only may well lead to a preference for high
input–high output systems, which, when concentrated at
regional scale, have been shown to cause major pollution
problems (Tamminga, 2003).
4.4. Differences within modes of expression
Within two of the three modes of expression major
differences in the rankings were observed (Table 12). For the
methods expressing results for the farm as a whole, EMA-PA
differs from DIALECTE for its global objective (assessment
of adherence to best practice for EMA, evaluation of
environmental impact for DIALECTE), for the environmental objectives considered (Table 3) and the indicators
used (Table 4). EMA-PA considers eight environmental
objectives, and DIALECTE nineteen, but the methods have
only two environmental objectives in common (Table 3).
Thus here both the different global objectives and the nearly
total disagreement with respect to the set of environmental
objectives may have caused the different rankings.
LCA, EF, EMA-EI and FarmSmart, which express results
both per ha and per kg of pig produced, yielded identical
rankings when results were expressed per ha. When results
were expressed per kg of pig produced, contrasting rankings
were obtained, or scenarios were not differentiated. These
methods are based on a limited number of environmental
objectives most of which are not shared (Table 3). So here
337
differences in the set of environmental objectives will
probably have contributed to the different rankings.
However, differences in calculation algorithms for the
indicators used and in the way the boundaries of the system
to be analysed were defined will also have played a major
role, as can bee seen from the contrasting results obtained by
the emissions inventories produced by three of these four
methods (Table 13). As the four methods differ both for their
set of objectives, indicators used and system definition, it is
not possible to assess the relative contribution of each of
these factors to the contrasting rankings obtained.
5. Conclusions
This work has clearly demonstrated that the outcome of
studies using indicator-based environmental evaluation
methods to compare farming systems depends not only
on the characteristics of the systems compared, but also to a
large extent on those of the evaluation methods used. Five
methods for evaluation of the environmental impacts of
farms were used to rank three farm scenarios. Depending on
the method, rankings obtained were similar, somewhat
different or completely inverse, or ranking proved inconclusive.
Outcomes differed due to differences in the evaluation
methods concerning: (i) the global objective of the method,
(ii) the set of environmental objectives considered, (iii) the
definition of the boundaries of the system to be analysed, (iv)
the calculation algorithms of the indicators used as
evaluation criteria. As the methods compared differed for
more than one of these characteristics, it was not possible to
assess the relative importance of the contribution of each of
these four factors.
This study furthermore revealed the mode of expression
of results (for the whole farm, per unit area, or per unit
product) as a fifth factor strongly affecting the rankings
obtained. Expression of impacts per unit area is complementary to expression per unit product. Reliance on the sole
impact/unit area ratio may well lead to a preference for low
input-low output systems, which may decrease impacts at
regional level, but may create a need for additional land use
elsewhere, giving rise to additional impacts. On the other
hand, reliance on the impact/unit product ratio only may
well lead to a preference for high input-high output systems,
which, when concentrated at regional scale, have been
shown to cause major pollution problems.
We therefore recommend the use of evaluation methods
that express their results both per unit area and per unit
product. More generally we recommend that environmental
evaluation methods be used with great caution. Users should
carefully consider which method is most appropriate given
their particular needs, taking into consideration the method’s
global objective, its system definition, its set of environmental objectives, the quality of the indicators used and its
mode of expression of results.
338
H.M.G. van der Werf et al. / Agriculture, Ecosystems and Environment 118 (2007) 327–338
Acknowledgements
This research was funded by an OECD research
fellowship within the OECD Co-operative Research
Programme: Biological Resource Management for Sustainable Agriculture Systems. The authors are solely responsible
for the data and opinion herein presented, that do not
represent the opinion of OECD. This work is part of the
research programme ‘‘Porcherie Verte’’ (Green Piggery) and
was financially supported by ADEME (Agence de
l’Environnement et de la Maı̂trise de l’Energie) and
OFIVAL (Office National Interprofessionnel des Viandes,
de l’Elevage et de l’Aviculture).
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